Revver Founder Launches Thoof: Personalized News Service

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The web is littered with failed or stagnant personalized news startups. New startup Thoof is going to give it a whirl and see if they can do better than the ones that have tried it before.

The idea around personalized news: instead of showing stuff based on what an editor chooses (like this blog, or the Washington Post) or via some sort of community action (Digg, reddit), a user will see news items that the service thinks you will like, based on your past behavior compared to the community at large.

Services that we’ve covered that have entered this space in one way or another include Searchfox (deadpool, assets acquired by Yahoo), Findory (deadpool), Spotback (change in strategy) and Feeds 2.0 (no idea what their status is, site is live).

I have my own reasons for explaining why, so far, these sites haven’t succeeded. I think people usually want to read news and then discuss it with friends. So what is considered “interesting” is influenced by what everyone else is consuming that day. People flock to the big news sites because everyone else flocks there, too, and the niche audiences that really want personalized news aren’t enough to sustain these startups.

But others disagree, including Thoof founder Ian Clarke. Clarke, formerly a co-founder of video site Revver, thinks the sites that came before Thoof simply didn’t have a good solution, and users were left wanting.

Thoof is all about personalized news, and it learns over time what you want to read. But it’s also part wiki and it has Digg/del.icio.us-like attributes as well.

News is submitted by users in a Digg-like fashion. A link to a news item is submitted, along with a title, description and tags. Other users start to see the news item if Thoof determines they will like it. However, submissions can be easily be edited by other users who think there is something lacking. Any aspect of the news item can be changed, including the link, in a wiki-like fashion (see screen shot above and to left). Other users will see the change and be asked to vote on it. If enough users say yes, the changes stand. Otherwise, it reverts back to the previous submission.

Thoof determines what you like based solely on what stories you click on to read. Asking for specific feedback, like voting or rating of stories, is too much to ask of users, Clarke says, noting that only a very small percentage of people who watched videos on Revver ever actually rated them. By analyzing what you tend to click on, Thoof will return results that it thinks you are more likely to click on than others. The result, over time, is a perfectly tailored news page for an individual. See the screen shot below.

So will this work? Clarke argues it will. in an email exchange where we were debating my position (the masses want popular news) v. his (the masses want tailored news), he writes:

Historically, news has been delivered in a one-to-many manner, meaning that lots of people tend to get the same news at the same time, but I think this is more of a bug than a feature. People don’t necessarily *want* to be shown the same stuff that everyone else is seeing, but the limitations of the technology somewhat required that this be the case. They would much rather see things that are specifically tailored to their interests, its just that either that option hasn’t existed, or it has been poorly executed.

The company is based in Austin and has five employees. They’ve raised a $1 million round of financing from Austin Ventures, Ron Conway and others. They launch today by invite only. Like Gmail and Joost, active users can invite a limited number of others…we’re working on getting some invites for readers.

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Crunchbase

OverviewThoof is a late entry to the personalized/social news arena. As many Digg clones and alternative models are failing or have failed to take hold of a substantial user base, Thoof brings a slightly new take on getting your daily stories which it hopes will attract more than a niche audience. Thoof is an automated news recommender of sorts. It works similarly to many music recommendation engines such …